Culinary Image Recognition

Overview

"EfficientFoodNet" is a deep learning project focused on classifying images of food into various categories. Utilizing TensorFlow, Keras, and the pre-trained EfficientNetB0 model, this project demonstrates the power of convolutional neural networks (CNNs) in computer vision tasks.

Project Description

The project employs the EfficientNetB0 architecture, a state-of-the-art CNN from TensorFlow's model garden. This model is known for its efficiency and high accuracy in image classification tasks. The network is used as a feature extractor where the top layer is replaced with a new classifier tailored for our specific task — recognizing different types of food.

Data Preparation and Augmentation

The dataset consists of images from 10 food categories, each represented by a small subset of training examples. To mitigate overfitting due to the small size of the dataset, extensive data augmentation techniques such as random flipping, rotation, zooming, and height/width shifting are applied.

Model Training and Evaluation

Two main experiments are conducted:

Model 0: A transfer learning model using EfficientNetB0 with its weights frozen, trained on 10% of the data.

Model 1 and Model 2: These models include the data augmentation pipeline within the model itself, allowing for more dynamic learning. They are trained on 1% and 10% of the data, respectively.

Each model's performance is evaluated based on its accuracy and the loss during training and validation phases. Additionally, fine-tuning is applied to the last few layers of the base model to further enhance the model's ability to generalize.

Tools and Libraries Used

TensorFlow and Keras for building and training the neural network.

Matplotlib for visualization of the data and results.

Pandas for data manipulation and analysis.

Google Colab for a cloud-based development environment, ensuring access to high-performance computing resources.

Conclusion

"EfficientFoodNet" demonstrates a robust approach to food image classification, highlighting the effectiveness of transfer learning combined with real-time data augmentation. The project outlines methodologies that can be adapted to similar image recognition tasks within the culinary domain or beyond.